28,063 research outputs found
Towards trajectory anonymization: a generalization-based approach
Trajectory datasets are becoming popular due to the massive usage of GPS and locationbased services. In this paper, we address privacy issues regarding the identification of individuals in static trajectory datasets. We first adopt the notion of k-anonymity to trajectories and propose a novel generalization-based approach for anonymization of trajectories. We further show that releasing
anonymized trajectories may still have some privacy leaks. Therefore we propose a randomization based reconstruction algorithm for releasing anonymized trajectory data and also present how the underlying techniques can be adapted to other anonymity standards. The experimental results on real and synthetic trajectory datasets show the effectiveness of the proposed techniques
TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer
In this work, we address the problem of musical timbre transfer, where the
goal is to manipulate the timbre of a sound sample from one instrument to match
another instrument while preserving other musical content, such as pitch,
rhythm, and loudness. In principle, one could apply image-based style transfer
techniques to a time-frequency representation of an audio signal, but this
depends on having a representation that allows independent manipulation of
timbre as well as high-quality waveform generation. We introduce TimbreTron, a
method for musical timbre transfer which applies "image" domain style transfer
to a time-frequency representation of the audio signal, and then produces a
high-quality waveform using a conditional WaveNet synthesizer. We show that the
Constant Q Transform (CQT) representation is particularly well-suited to
convolutional architectures due to its approximate pitch equivariance. Based on
human perceptual evaluations, we confirmed that TimbreTron recognizably
transferred the timbre while otherwise preserving the musical content, for both
monophonic and polyphonic samples.Comment: 17 pages, published as a conference paper at ICLR 201
The wonderland of reflections
A fundamental fact for the algebraic theory of constraint satisfaction
problems (CSPs) over a fixed template is that pp-interpretations between at
most countable \omega-categorical relational structures have two algebraic
counterparts for their polymorphism clones: a semantic one via the standard
algebraic operators H, S, P, and a syntactic one via clone homomorphisms
(capturing identities). We provide a similar characterization which
incorporates all relational constructions relevant for CSPs, that is,
homomorphic equivalence and adding singletons to cores in addition to
pp-interpretations. For the semantic part we introduce a new construction,
called reflection, and for the syntactic part we find an appropriate weakening
of clone homomorphisms, called h1 clone homomorphisms (capturing identities of
height 1).
As a consequence, the complexity of the CSP of an at most countable
-categorical structure depends only on the identities of height 1
satisfied in its polymorphism clone as well as the the natural uniformity
thereon. This allows us in turn to formulate a new elegant dichotomy conjecture
for the CSPs of reducts of finitely bounded homogeneous structures.
Finally, we reveal a close connection between h1 clone homomorphisms and the
notion of compatibility with projections used in the study of the lattice of
interpretability types of varieties.Comment: 24 page
Locally Non-linear Embeddings for Extreme Multi-label Learning
The objective in extreme multi-label learning is to train a classifier that
can automatically tag a novel data point with the most relevant subset of
labels from an extremely large label set. Embedding based approaches make
training and prediction tractable by assuming that the training label matrix is
low-rank and hence the effective number of labels can be reduced by projecting
the high dimensional label vectors onto a low dimensional linear subspace.
Still, leading embedding approaches have been unable to deliver high prediction
accuracies or scale to large problems as the low rank assumption is violated in
most real world applications.
This paper develops the X-One classifier to address both limitations. The
main technical contribution in X-One is a formulation for learning a small
ensemble of local distance preserving embeddings which can accurately predict
infrequently occurring (tail) labels. This allows X-One to break free of the
traditional low-rank assumption and boost classification accuracy by learning
embeddings which preserve pairwise distances between only the nearest label
vectors.
We conducted extensive experiments on several real-world as well as benchmark
data sets and compared our method against state-of-the-art methods for extreme
multi-label classification. Experiments reveal that X-One can make
significantly more accurate predictions then the state-of-the-art methods
including both embeddings (by as much as 35%) as well as trees (by as much as
6%). X-One can also scale efficiently to data sets with a million labels which
are beyond the pale of leading embedding methods
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